scholarly journals Information Integration and Collective Motility in Phototactic Cyanobacteria

2019 ◽  
Author(s):  
S. N. Menon ◽  
P. Varuni ◽  
G I. Menon

AbstractCells in microbial colonies integrate information across multiple spatial and temporal scales while sensing environmental cues. A number of photosynthetic cyanobacteria respond in a directional manner to incident light, resulting in the phototaxis of individual cells. Colonies of such bacteria exhibit large-scale changes in morphology, arising from cell-cell interactions, during phototaxis. These interactions occur through type IV pili-mediated physical contacts between cells, as well as through the secretion of complex polysaccharides (‘slime’) that facilitates cell motion. Here, we describe a computational model for such collective behaviour in colonies of the cyanobacteriumSynechocystis. The model is designed to replicate observations from recent experiments on the emergent response of the colonies to varied light regimes. It predicts the complex colony morphologies that arise as a result. We ask if changes in colony morphology during phototaxis can be used to infer if cells integrate information from multiple light sources simultaneously, or respond to these light sources separately at each instant of time. We find that these two scenarios cannot be distinguished from the shapes of colonies alone. However, we show that tracking the trajectories of individual cyanobacteria provides a way of determining their mode of response. Our model allows us to address the emergent nature of this class of collective bacterial motion, linking individual cell response to the dynamics of colony shape.Statement of SignificanceMicrobial colonies in the wild often consist of large groups of heterogeneous cells that coordi-nate and integrate information across multiple spatio-temporal scales. We describe a computational model for one such collective behaviour, phototaxis, in colonies of the cyanobacteriumSynechocystisthat move in response to light. The model replicates experimental observations the response of cyanobacterial colonies to varied light regimes, and predicts the complex colony morphologies that arise as a result. The results suggest that tracking the trajectories of individual cyanobacteria may provide a way of determining their mode of information integration. Our model allows us to address the emergent nature of this class of collective bacterial motion, linking individual cell response to the large scale dynamics of the colony.

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2771
Author(s):  
Leszek Kotulski ◽  
Artur Basiura ◽  
Igor Wojnicki ◽  
Sebastian Siuchta

The use of formal methods and artificial intelligence has made it possible to automatically design outdoor lighting. Quick design for large cities, in a matter of hours instead of weeks, and analysis of various optimization criteria enables to save energy and tune profit stream from lighting retrofit. Since outdoor lighting is of a large scale, having luminaires on every street in urban areas, and since it needs to be retrofitted every 10 to 15 years, choosing proper parameters and light sources leads to significant energy savings. This paper presents the concept and calculations of Levelized Cost of Electricity for outdoor lighting retrofit. It is understood as cost of energy savings, it is in the range from 23.06 to 54.64 EUR/MWh, based on real-world cases. This makes street and road lighting modernization process the best green “energy source” if compared with the 2018 Fraunhofer Institute cost of electricity renewable energy technologies ranking. This indicates that investment in lighting retrofit is more economically and ecologically viable than investment in new renewable energy sources.


2014 ◽  
Vol 11 (95) ◽  
pp. 20140043 ◽  
Author(s):  
Giancarlo De Luca ◽  
Patrizio Mariani ◽  
Brian R. MacKenzie ◽  
Matteo Marsili

Animals form groups for many reasons, but there are costs and benefits associated with group formation. One of the benefits is collective memory. In groups on the move, social interactions play a crucial role in the cohesion and the ability to make consensus decisions. When migrating from spawning to feeding areas, fish schools need to retain a collective memory of the destination site over thousands of kilometres, and changes in group formation or individual preference can produce sudden changes in migration pathways. We propose a modelling framework, based on stochastic adaptive networks, that can reproduce this collective behaviour. We assume that three factors control group formation and school migration behaviour: the intensity of social interaction, the relative number of informed individuals and the strength of preference that informed individuals have for a particular migration area. We treat these factors independently and relate the individuals’ preferences to the experience and memory for certain migration sites. We demonstrate that removal of knowledgeable individuals or alteration of individual preference can produce rapid changes in group formation and collective behaviour. For example, intensive fishing targeting the migratory species and also their preferred prey can reduce both terms to a point at which migration to the destination sites is suddenly stopped. The conceptual approaches represented by our modelling framework may therefore be able to explain large-scale changes in fish migration and spatial distribution.


2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Andrew Chalmers ◽  
Snjezana Soltic

This paper is concerned with designing light source spectra for optimum luminous efficacy and colour rendering. We demonstrate that it is possible to design light sources that can provide both good colour rendering and high luminous efficacy by combining the outputs of a number of narrowband spectral constituents. Also, the achievable results depend on the numbers and wavelengths of the different spectral bands utilized in the mixture. Practical realization of these concepts has been demonstrated in this pilot study which combines a number of simulations with tests using real LEDs (light emitting diodes). Such sources are capable of providing highly efficient lighting systems with good energy conservation potential. Further research is underway to investigate the practicalities of our proposals in relation to large-scale light source production.


2010 ◽  
Vol 37 (5) ◽  
pp. 403 ◽  
Author(s):  
Craig R. Brodersen ◽  
Thomas C. Vogelmann

Leaf anatomy plays a functional role in propagating light through the leaf; palisade mesophyll has been shown to facilitate the channelling of collimated light deeper into the spongy mesophyll. Direct measurements of the propagation of diffuse light into the leaf, however, are absent. Using chlorophyll fluorescence imaging of leaf cross-sections, we measured light absorption profiles in leaves under direct (collimated), diffuse and low-angle monochromatic light. Low-angle and diffuse light was absorbed closer to the irradiated surface than direct light perpendicular to the surface. The shapes of internal absorption profiles indicated that leaves were influenced by the directional quality of the incident light. In addition, absorption profiles revealed that leaves were not simple light absorbing objects and that cellular anatomy influences the direction of light travelling into the mesophyll. These findings also suggest a mechanism for previously measured differences in leaf level photosynthesis under opposing light regimes.


2016 ◽  
Vol 40 (7) ◽  
pp. 867-881 ◽  
Author(s):  
Dingguo Yu ◽  
Nan Chen ◽  
Xu Ran

Purpose With the development and application of mobile internet access, social media represented by Weibo, WeChat, etc. has become the main channel for information release and sharing. High-impact users in social networks are key factors stimulating the large-scale propagation of information within social networks. User influence is usually related to the user’s attention rate, activity level, and message content. The paper aims to discuss these issues. Design/methodology/approach In this paper, the authors focused on Sina Weibo users, centered on users’ behavior and interactive information, and formulated a weighted interactive information network model, then present a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc., the model incorporated the time dimension, through the calculation of users’ attribute influence and interactive influence, to comprehensively measure the user influence of Sina Weibo users. Findings Compared with other models, the model reflected the dynamics and timeliness of the user influence in a more accurate way. Extensive experiments are conducted on the real-world data set, and the results validate the performance of the approach, and demonstrate the effectiveness of the dynamics and timeliness. Due to the similarity in platform architecture and user behavior between Sina Weibo and Twitter, the calculation model is also applicable to Twitter. Originality/value This paper presents a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc.


2010 ◽  
Vol 365 (1550) ◽  
pp. 2267-2278 ◽  
Author(s):  
N. Owen-Smith ◽  
J. M. Fryxell ◽  
E. H. Merrill

We outline how principles of optimal foraging developed for diet and food patch selection might be applied to movement behaviour expressed over larger spatial and temporal scales. Our focus is on large mammalian herbivores, capable of carrying global positioning system (GPS) collars operating through the seasonal cycle and dependent on vegetation resources that are fixed in space but seasonally variable in availability and nutritional value. The concept of intermittent movement leads to the recognition of distinct movement modes over a hierarchy of spatio-temporal scales. Over larger scales, periods with relatively low displacement may indicate settlement within foraging areas, habitat units or seasonal ranges. Directed movements connect these patches or places used for other activities. Selection is expressed by switches in movement mode and the intensity of utilization by the settlement period relative to the area covered. The type of benefit obtained during settlement periods may be inferred from movement patterns, local environmental features, or the diel activity schedule. Rates of movement indicate changing costs in time and energy over the seasonal cycle, between years and among regions. GPS telemetry potentially enables large-scale movement responses to changing environmental conditions to be linked to population performance.


2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Bo-Ya Ji ◽  
Zhu-Hong You ◽  
Han-Jing Jiang ◽  
Zhen-Hao Guo ◽  
Kai Zheng

Abstract Background The prediction of potential drug-target interactions (DTIs) not only provides a better comprehension of biological processes but also is critical for identifying new drugs. However, due to the disadvantages of expensive and high time-consuming traditional experiments, only a small section of interactions between drugs and targets in the database were verified experimentally. Therefore, it is meaningful and important to develop new computational methods with good performance for DTIs prediction. At present, many existing computational methods only utilize the single type of interactions between drugs and proteins without paying attention to the associations and influences with other types of molecules. Methods In this work, we developed a novel network embedding-based heterogeneous information integration model to predict potential drug-target interactions. Firstly, a heterogeneous multi-molecuar information network is built by combining the known associations among protein, drug, lncRNA, disease, and miRNA. Secondly, the Large-scale Information Network Embedding (LINE) model is used to learn behavior information (associations with other nodes) of drugs and proteins in the network. Hence, the known drug-protein interaction pairs can be represented as a combination of attribute information (e.g. protein sequences information and drug molecular fingerprints) and behavior information of themselves. Thirdly, the Random Forest classifier is used for training and prediction. Results In the results, under the five-fold cross validation, our method obtained 85.83% prediction accuracy with 80.47% sensitivity at the AUC of 92.33%. Moreover, in the case studies of three common drugs, the top 10 candidate targets have 8 (Caffeine), 7 (Clozapine) and 6 (Pioglitazone) are respectively verified to be associated with corresponding drugs. Conclusions In short, these results indicate that our method can be a powerful tool for predicting potential drug-target interactions and finding unknown targets for certain drugs or unknown drugs for certain targets.


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